code: def _validate_center_shape(X, n_centers, centers):
      """Check if centers is compatible with X and n_centers"""
      if centers.shape[0] != n_centers:
          raise ValueError(
              f"The shape of the initial centers {centers.shape} does not "
              f"match the number of clusters {n_centers}."
          )
      if centers.shape[1] != X.shape[1]:
          raise ValueError(
              f"The shape of the initial centers {centers.shape} does not "
              f"match the number of features of the data {X.shape[1]}."
          )
  
imports: import numbers
  import warnings
  import numpy
  from scipy import sparse
  from sklearn.cluster import KMeans
  from sklearn.cluster._kmeans import _labels_inertia
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.utils import check_array, check_random_state
  from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
  from sklearn.utils.extmath import row_norms
  from sklearn.utils.sparsefuncs import mean_variance_axis
  from sklearn.utils.validation import _deprecate_positional_args, _num_samples, check_is_fitted
  import daal4py
  from .._device_offload import support_usm_ndarray
  from .._utils import PatchingConditionsChain, control_n_jobs, getFPType, run_with_n_jobs, sklearn_check_version
label_desc: Use image node_id 1 for calling a global function with example usage: _validate_center_shape(X, n_centers, centers) without return types